change readme

README.md

@@ -1,8 +1,10 @@
-# 介绍
+# Introduction
 
-本项目主要是介绍如何结合 flux 和 controlnet 进行 inpaint，以童装场景为示例。更多详细的介绍请参考：[yishaoai/tutorials-of-100-wonderful-ai-models](https://github.com/yishaoai/tutorials-of-100-wonderful-ai-models/)的第三节。
+[中文版本](https://github.com/yishaoai/flux-controlnet-inpaint/blob/main/README_ZH.md)
+This project mainly introduces how to combine flux and controlnet for inpaint, taking the children's clothing scene as an example. For more detailed introduction, please refer to the third section of [yishaoai/tutorials-of-100-wonderful-ai-models](https://github.com/yishaoai/tutorials-of-100-wonderful-ai-models/).
+
+The usage of this project is as follows:
 
-本项目的使用方法如下：
 ```python
 import torch
 from diffusers.utils import load_image
@@ -36,10 +38,10 @@ image_res = pipe(
     joint_attention_kwargs={"scale": 0.8},
     ).images[0]
 ```
+### Result example
+The following example image is based on the children's clothing scene. The clothing part will remain unchanged, and the portrait and background will be redrawn based on the prompt word. Because controlnet is used, the edges of these images will be similar. The weight of controlnet can be adjusted based on the need. The larger the weight, the more edge information of the generated image is retained, and the smaller the weight, the less edge information is retained.
+The inpaint method is suitable for reproducing popular products, which is very suitable for the needs of buyers' shows and users in the e-commerce field. At the same time, it can also provide merchants with the main picture of clothing models.
 
-### 结果示例
-以下示例图是以童装场景为例，会把衣服部分保持不变，将人像和背景基于提示词进行重绘。因为用到controlnet，所以这些图像的边缘会相似。基于需要controlnet的权重可以进行调整，权重越大，生成图像的边缘信息保留越多，权重越小，边缘信息保留越少。
-inpaint方法适合对爆款产品进行复刻，十分适合电商领域的买家秀和种草的用户需求。同时，也可以给商家朋友提供服装模特的主图。
 
 ![demo](https://github.com/yishaoai/flux-controlnet-inpaint/blob/main/assets/flux-controlnet-inpaint.png)
 

README_ZH.md

@@ -0,0 +1,45 @@
+# 介绍
+
+本项目主要是介绍如何结合 flux 和 controlnet 进行 inpaint，以童装场景为示例。更多详细的介绍请参考：[yishaoai/tutorials-of-100-wonderful-ai-models](https://github.com/yishaoai/tutorials-of-100-wonderful-ai-models/)的第三节。
+
+本项目的使用方法如下：
+```python
+import torch
+from diffusers.utils import load_image
+from diffusers.pipelines.flux.pipeline_flux_controlnet_inpaint import FluxControlNetInpaintPipeline
+from diffusers.models.controlnet_flux import FluxControlNetModel
+from controlnet_aux import CannyDetector
+
+base_model = 'black-forest-labs/FLUX.1-dev'
+controlnet_model = 'YishaoAI/flux-dev-controlnet-canny-kid-clothes'
+
+pipe = FluxControlNetInpaintPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
+pipe.to("cuda")
+
+image = load_image(image_path)
+mask = load_image(mask_path)
+canny = CannyDetector()
+canny_image = canny(image)
+prompt = "children's clothing model"
+
+image_res = pipe(
+    prompt,
+    image=image,
+    control_image=canny_image,
+    controlnet_conditioning_scale=0.5,
+    mask_image=mask,
+    strength=0.95,
+    num_inference_steps=50,
+    guidance_scale=5,
+    generator=generator,
+    joint_attention_kwargs={"scale": 0.8},
+    ).images[0]
+```
+
+### 结果示例
+以下示例图是以童装场景为例，会把衣服部分保持不变，将人像和背景基于提示词进行重绘。因为用到controlnet，所以这些图像的边缘会相似。基于需要controlnet的权重可以进行调整，权重越大，生成图像的边缘信息保留越多，权重越小，边缘信息保留越少。
+inpaint方法适合对爆款产品进行复刻，十分适合电商领域的买家秀和种草的用户需求。同时，也可以给商家朋友提供服装模特的主图。
+
+![demo](https://github.com/yishaoai/flux-controlnet-inpaint/blob/main/assets/flux-controlnet-inpaint.png)
+